TY - JOUR
T1 - Scheduling of multiple in-line steppers for semiconductor wafer fabs
AU - Chiou, Chie Wun
AU - Wu, Muh-Cherng
PY - 2014/3/1
Y1 - 2014/3/1
N2 - A few prior studies noticed that an in-line stepper (a bottleneck machine in a semiconductor fab) may have a capacity loss while operated in a low-yield scenario. To alleviate such a capacity loss, some meta-heuristic algorithms for scheduling a single in-line stepper were proposed. Yet, in practice, there are multiple in-line steppers to be scheduled in a fab. This article aims to enhance prior algorithms so as to deal with the scheduling for multiple in-line steppers. Compared to prior studies, this research has to additionally consider how to appropriately allocate jobs to various machines. We enhance prior algorithms by developing a chromosome-decoding scheme which can yield a job-allocation decision for any given chromosome (or job sequence). Seven enhanced versions of meta-heuristic algorithms (genetic algorithm, Tabu, GA-Tabu, simulated annealing, M-MMAX, PACO and particle swarm optimisation) were then proposed and tested. Numerical experiments indicate that the GA-Tabu method outperforms the others. In addition, the lower the process yield, the better is the performance of the GA-Tabu algorithm.
AB - A few prior studies noticed that an in-line stepper (a bottleneck machine in a semiconductor fab) may have a capacity loss while operated in a low-yield scenario. To alleviate such a capacity loss, some meta-heuristic algorithms for scheduling a single in-line stepper were proposed. Yet, in practice, there are multiple in-line steppers to be scheduled in a fab. This article aims to enhance prior algorithms so as to deal with the scheduling for multiple in-line steppers. Compared to prior studies, this research has to additionally consider how to appropriately allocate jobs to various machines. We enhance prior algorithms by developing a chromosome-decoding scheme which can yield a job-allocation decision for any given chromosome (or job sequence). Seven enhanced versions of meta-heuristic algorithms (genetic algorithm, Tabu, GA-Tabu, simulated annealing, M-MMAX, PACO and particle swarm optimisation) were then proposed and tested. Numerical experiments indicate that the GA-Tabu method outperforms the others. In addition, the lower the process yield, the better is the performance of the GA-Tabu algorithm.
KW - flow shop
KW - genetic algorithm
KW - meta-heuristic algorithms
KW - port capacity constraints
KW - scheduling
KW - semiconductor
UR - http://www.scopus.com/inward/record.url?scp=84884888747&partnerID=8YFLogxK
U2 - 10.1080/00207721.2012.724093
DO - 10.1080/00207721.2012.724093
M3 - Article
AN - SCOPUS:84884888747
VL - 45
SP - 384
EP - 398
JO - International Journal of Systems Science
JF - International Journal of Systems Science
SN - 0020-7721
IS - 3
ER -